Conventional foreground and background separation technologies are configured to separate a foreground and a background by modeling a background using the brightness values of pixels. However, since a complicated background is not easy to model, the background is modeled using the Mixture of Gaussians (MOG) method. The MOG method is disadvantageous in that it is difficult to model a rapid change in background using a small number of Gaussians. In contrast, if in order to overcome this problem, the number of Gaussians is increased and the learning ratio is adjusted to the rapid change, another problem arises in that a slowly changing background is determined to be a foreground.
Furthermore, since the technology which is actually applied focuses on the speed, it creates a background model depending on whether a condition is fulfilled. In this case, when a background model is created, an elaborate model is not created and, an excessive number of models are created, so that many models must be searched through, thus resulting in a loss of time.